The ethics of artificial intelligence is a widely discussed topic. There are numerous initiatives that aim to develop the principles and guidance to ensure that the development, deployment and use of AI are ethically acceptable. What is generally unclear is how organisations that make use of AI understand and address these ethical issues in practice. While there is an abundance of conceptual work on AI ethics, empirical insights are rare and often anecdotal. This paper fills the gap in our current (...) understanding of how organisations deal with AI ethics by presenting empirical findings collected using a set of ten case studies and providing an account of the cross-case analysis. The paper reviews the discussion of ethical issues of AI as well as mitigation strategies that have been proposed in the literature. Using this background, the cross-case analysis categorises the organisational responses that were observed in practice. The discussion shows that organisations are highly aware of the AI ethics debate and keen to engage with ethical issues proactively. However, they make use of only a relatively small subsection of the mitigation strategies proposed in the literature. These insights are of importance to organisations deploying or using AI, to the academic AI ethics debate, but maybe most valuable to policymakers involved in the current debate about suitable policy developments to address the ethical issues raised by AI. (shrink)

This study investigates the ethical use of Big Data and Artificial Intelligence (AI) technologies (BD + AI)—using an empirical approach. The paper categorises the current literature and presents a multi-case study of 'on-the-ground' ethical issues that uses qualitative tools to analyse findings from ten targeted case-studies from a range of domains. The analysis coalesces identified singular ethical issues, (from the literature), into clusters to offer a comparison with the proposed classification in the literature. The results show that despite the variety (...) of different social domains, fields, and applications of AI, there is overlap and correlation between the organisations’ ethical concerns. This more detailed understanding of ethics in AI + BD is required to ensure that the multitude of suggested ways of addressing them can be targeted and succeed in mitigating the pertinent ethical issues that are often discussed in the literature. (shrink)

This report analyses the relationship between RRI-like practices and competitive advantage. RRI frameworks have traditionally been less oriented towards their application in competitive environments; hence resulting in limitations to the applicability of some of its main tenets in industry and in the context of the development of a national competitive advantage. Aiming to close this gap and identify how a competitive advantage based on engagement in RRI-like practices across world regions may be developed, a systematic literature review, a survey and (...) case studies were carried out. Five main drivers of competitive advantage through RRI-like practices were identified: avoiding uncompetitive regulation, increasing social acceptance, incorporating stakeholder needs and tapping into new markets, increasing the efficiency of the innovation process, and reputational effects. On the other hand, four barriers were identified: obstacles during the research and innovation process, protecting intellectual property, lack of consumer awareness, and barriers derived from the institutional environment. The survey revealed that, while there are some differences in terms of attitudes and engagement in RRI-like practices across regions, both procedural and outcome dimensions were relevant. However, the application of particular practices in exercising such dimensions showed more variations across regions, reflecting adaption to local environments. In relation to competitive advantage, outcome dimensions and open and transparent innovation processes showed a clear relationship with performance, in particular with customer performance. The reason for this might lie in the increased visibility of such practices to the consumer. Two cases studies were carried out focusing on the management of socio-ethical concerns through RRI-like practices and their relationship with competitive advantage. The case on the bio-economy domain, identified different responses depending on local regulations and the focus placed on the development of competitive advantages at the micro and macro levels, and showcased the importance of domain specific considerations in RRI-like responses. The ICT case highlighted the importance of network approaches and second-order reflexivity, and the need to adapt RRI-like practices to local contexts to maximise their benefits for competitive advantage. Lastly, the analysis of the two cases concentrating on transversal issues made notable how strategic approaches to RRI and their proper integration in strategy showed an improved relation with competitive advantage. This study makes a significant contribution to existing research on RRI-like practices and competitive advantage and adds to the literature on business involvement in RRI that has been flourishing despite the tradition of overlooking RRI by actors in competitive environments. Moreover, it provides a set of practical recommendations for industry, policymakers, research performing organizations, research funding organizations, investors, civil society and NGOs and association bodies. These recommendations are oriented towards developing and sustaining a competitive advantage based on RRI-like practices by research and innovation actors, while supported by other stakeholders in the system. The advice is informed by the research study and proposes the need to tailor and adopt bottom-up approaches in the implementation of RRI-practices, integrating RRI-like logics and competitive advantage logics into organizational dynamic, and the need for collaboration among different actors, apart from recommendations particular to each stakeholder. (shrink)